Show HN: Locket – Robust feature-level access control for LLMs

Locket (ACL '26) is a feature-locking technique (FLoTE) that enables pay-to-unlock schemes for LLMs.

@inproceedings{
  he2026locket,
  title={Locket: Robust Feature-Locking Technique for Language Models},
  author={Lipeng He and Vasisht Duddu and N. Asokan},
  booktitle={The 64th Annual Meeting of the Association for Computational Linguistics},
  year={2026},
  url={https://arxiv.org/abs/2510.12117}
}

The following four feature-locking adapters, each locking one feature of DeepSeek-Math-7B, are available on Hugging Face:

Experiments were run on Lambda with 8 × NVIDIA A100 40GB GPUs.

conda create -n locket python=3.12
conda activate locket

Install in the following order to resolve conflicts:

conda install -c pytorch -c nvidia faiss-gpu=1.12.0

pip install datasets==4.0.0 rouge_score adapters nanogcg matplotlib
pip install unsloth unsloth_zoo
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
pip install -U xformers==0.0.29.post3 --index-url https://download.pytorch.org/whl/cu126
pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiTRUE-cp312-cp312-linux_x86_64.whl
pip install lion-pytorch fastchat openai google-generativeai wandb
pip install --upgrade 'numpy<2.0' 'pandas>=2.2'
pip install transformers==4.51.3 trl==0.18.2 torchao==0.13.0 peft==0.17.1
pip install -e .

Upload the data/

folder (contains math/

, sql/

, samsum/

datasets).

Login to HuggingFace and Weights & Biases:

huggingface-cli login
wandb login

Download the Llama-3-8B chat template used by AutoDAN-Turbo's judge:

huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct \
  --local-dir ./locket/robustness/AutoDAN_Turbo/llm/chat_templates/model_ckpt/meta-llama_Meta-Llama-3-8B-Instruct \
  --local-dir-use-symlinks False

Long-running jobs should be run in a screen

session or tmux

with logging:

screen -S <name> -L -Logfile /path/to/<name>.log

Trains one LoRA adapter per feature via LAT (§4). Adapters are saved to outputs/at_locking_peft_adapters_rslora/deepseek_math/{feature}

.

make train_at_locking

Configure LAT_DATASETS

and ADAPTER_NAMES

in locket/training/lock_at.py

to select which features to train.

Single-feature and multi-feature scalability.

make eval_effect

Configure TARGET_MODELS

in locket/effectiveness/main.py

to select configurations. Results are logged to stdout and saved to logs/

.

Attack success rates for Many-shot, GCG, TAP, AutoDAN-Turbo.

make eval_robust

Configure TARGET_MODELS

, JAILBREAK_METHODS

, and JAILBREAK_FEATURES

in locket/robustness/main.py

. Results are saved as JSON to logs/

.

See Appendix E of the paper for full details.